How normative interpretations of climate risk assessment affect local decision making: an exploratory study at the city scale in Cork, Ireland

Urban areas already suffer substantial losses in both economic and human terms from climate related disasters. These losses are anticipated to grow substantially, in part as a result of the impacts of climate change. In this paper we investigate the process of translating climate risk data into action for the city level. We apply a commonly used decision-framework as our backdrop and explore where in this process climate risk assessment and normative political judgments intersect. We use the case of flood risk management in Cork city in Ireland to investigate what is needed for translating risk assessment into action at the local city level. Evidence presented is based on focus group discussions at two stakeholder workshops, and a series of individual meetings and phone-discussions with stakeholders involved in local decision making related to flood risk management and adaptation to climate change, in Ireland. Respondents were chosen on the basis of their expertise and/or involvement in the decision making processes locally and nationally. Representatives of groups affected by flood risk and flood risk management/adaptation efforts were also included. The Cork example highlights that, despite ever more accurate data and an increasing range of theoretical approaches available to local decision makers, it is the normative interpretation of this information that determines what action is taken. The use of risk assessments for decision making is a process that requires normative decisions, such as setting ‘acceptable risk levels’ and identifying ‘adequate’ protection levels, which will not succeed without broader buy-in and stakeholder participation. Identifying and embracing those up-front could strengthen the urban adaptation process - this may in fact turn out to be the biggest advantage of climate risk assessment: it offers an opportunity to create a shared understanding of the problem and enables an informed evaluation and discussion of remedial action

What do we visually focus on in a World Heritage Site? A case study in the Historic Centre of Prague

Since socio-economic development is associated with artificial construction, heritage environments must be protected and renewed while adapting to such development. Many World Heritage Sites’ visual integrity is endangered by new construction. The paper aims to explore people’s visual focus patterns concerning the integrity of heritage to ensure that traditional culture is not endangered by the construction and development of modern life, and to protect Outstanding Universal Values. In this study, visual heatmaps are generated to investigate people’s visual integrity in the Historic Centre of Prague from micro to macro viewpoints using an eye tracker. We found that humans’ perspectives are unobstructed or concentrated, and the view of main attractions is generally maintained by a buffer zone. However, newly constructed high-rise buildings can result in major visual concerns. Therefore, new buildings with large heights and strong contrasting colours should be restricted to World Heritage Sites. Moreover, complex artistic effects (facade midline, domes, mural painting, faces of sculptures) will likely attract people’s attention. In contrast, visual focus is not concentrated on greenery, roofs and floors. Accordingly, greenery could become a flexible space to serve as a background for buildings and landscape nodes. Furthermore, visual focal factors are associated with two significant aspects: people and the environment. Since people and transportation could pose visual concerns, tourism managers should optimise for characteristics such as controlling the density of pedestrian flow and planning parking spaces. The visual patterns identified could be useful for the design, conservation, and management of visual integrity in cultural heritage sites to avoid the spread of artificial constructions within the boundaries of heritage sites, which may lead to their being endangered or delisted

A Framework Proposal for Regional-Scale Flood-Risk Assessment of Cultural Heritage Sites and Application to the Castile and León Region (Central Spain)

Floods, at present, may constitute the natural phenomenon with the greatest impact on the deterioration of cultural heritage, which is the reason why the study of flood risk becomes essential in any attempt to manage cultural heritage (archaeological sites, historic buildings, artworks, etc.) This management of cultural heritage is complicated when it is distributed over a wide territory. This is precisely the situation in the region of Castile and León (Spain), in which 2155 cultural heritage elements are registered in the Catalog of Cultural Heritage Sites of Castile and León, and these are distributed along the 94,226 km2 of this region. Given this scenario, the present study proposes a methodological framework of flood risk analysis for these cultural heritage sites and elements. This assessment is based on two main processing tools to be developed in addition: on the one hand, the creation of a GIS database in which to establish the spatial relationship between the cultural heritage elements and the flow-prone areas for different flood return periods and, on the other hand, the creation of a risk matrix in which different variables are regarded as associated both to flood hazard (return period, flow depth, and river flooding typology) and to flood vulnerability (construction typology, and construction structural relationship with the hydraulic environment). The combination of both tools has allowed us to establish each cultural heritage flood risk level, making its categorization of risk possible. Of all the cultural heritage sites considered, 18 of them are categorized under an Extreme flood risk level; and another 24 show a High potential flood risk level. Therefore, these are about 25% to 30% of all cultural heritage sites in Castile and León. This flood risk categorization, with a scientific basis of the cultural heritage sites at risk, makes it possible to define territories of high flood risk clustering; where local scale analyses for mitigation measures against flood risk are necessary

Towards an Uncertainty-Aware Visualization in the Digital Humanities

[EN]As visualization becomes widespread in a broad range of cross-disciplinary academic domains, such as the digital humanities (DH), critical voices have been raised on the perils of neglecting the uncertain character of data in the visualization design process. Visualizations that, purposely or not, obscure or remove uncertainty in its different forms from the scholars’ vision may negatively affect the manner in which humanities scholars regard computational methods as useful tools in their daily work. In this paper, we address the issue of uncertainty representation in the context of the humanities from a theoretical perspective, in an attempt to provide the foundations of a framework that allows for the construction of ecological interface designs which are able to expose the computational power of the algorithms at play while, at the same time, respecting the particularities and needs of humanistic research. To this end, we review past uncertainty taxonomies in other domains typically related to the humanities and visualization, such as cartography and GIScience. From this review, we select an uncertainty taxonomy related to the humanities that we link to recent research in visualization for the DH. Finally, we bring a novel analytics method developed by other authors (Progressive Visual Analytics) into question, which we argue can be a good candidate to resolve the aforementioned difficulties in DH practic

Smart City Ontologies and Their Applications: A Systematic Literature Review

The increasing interconnections of city services, the explosion of available urban data, and the need for multidisciplinary analysis and decision making for city sustainability require new technological solutions to cope with such complexity. Ontologies have become viable and effective tools to practitioners for developing applications requiring data and process interoperability, big data management, and automated reasoning on knowledge. We investigate how and to what extent ontologies have been used to support smart city services and we provide a comprehensive reference on what problems have been addressed and what has been achieved so far with ontology-based applications. To this purpose, we conducted a systematic literature review finalized to presenting the ontologies, and the methods and technological systems where ontologies play a relevant role in shaping current smart cities. Based on the result of the review process, we also propose a classification of the sub-domains of the city addressed by the ontologies we found, and the research issues that have been considered so far by the scientific community. We highlight those for which semantic technologies have been mostly demonstrated to be effective to enhance the smart city concept and, finally, discuss in more details about some open problems

Visual Analytics for the Exploratory Analysis and Labeling of Cultural Data

Cultural data can come in various forms and modalities, such as text traditions, artworks, music, crafted objects, or even as intangible heritage such as biographies of people, performing arts, cultural customs and rites. The assignment of metadata to such cultural heritage objects is an important task that people working in galleries, libraries, archives, and museums (GLAM) do on a daily basis. These rich metadata collections are used to categorize, structure, and study collections, but can also be used to apply computational methods. Such computational methods are in the focus of Computational and Digital Humanities projects and research. For the longest time, the digital humanities community has focused on textual corpora, including text mining, and other natural language processing techniques. Although some disciplines of the humanities, such as art history and archaeology have a long history of using visualizations. In recent years, the digital humanities community has started to shift the focus to include other modalities, such as audio-visual data. In turn, methods in machine learning and computer vision have been proposed for the specificities of such corpora. Over the last decade, the visualization community has engaged in several collaborations with the digital humanities, often with a focus on exploratory or comparative analysis of the data at hand. This includes both methods and systems that support classical Close Reading of the material and Distant Reading methods that give an overview of larger collections, as well as methods in between, such as Meso Reading. Furthermore, a wider application of machine learning methods can be observed on cultural heritage collections. But they are rarely applied together with visualizations to allow for further perspectives on the collections in a visual analytics or human-in-the-loop setting. Visual analytics can help in the decision-making process by guiding domain experts through the collection of interest. However, state-of-the-art supervised machine learning methods are often not applicable to the collection of interest due to missing ground truth. One form of ground truth are class labels, e.g., of entities depicted in an image collection, assigned to the individual images. Labeling all objects in a collection is an arduous task when performed manually, because cultural heritage collections contain a wide variety of different objects with plenty of details. A problem that arises with these collections curated in different institutions is that not always a specific standard is followed, so the vocabulary used can drift apart from another, making it difficult to combine the data from these institutions for large-scale analysis. This thesis presents a series of projects that combine machine learning methods with interactive visualizations for the exploratory analysis and labeling of cultural data. First, we define cultural data with regard to heritage and contemporary data, then we look at the state-of-the-art of existing visualization, computer vision, and visual analytics methods and projects focusing on cultural data collections. After this, we present the problems addressed in this thesis and their solutions, starting with a series of visualizations to explore different facets of rap lyrics and rap artists with a focus on text reuse. Next, we engage in a more complex case of text reuse, the collation of medieval vernacular text editions. For this, a human-in-the-loop process is presented that applies word embeddings and interactive visualizations to perform textual alignments on under-resourced languages supported by labeling of the relations between lines and the relations between words. We then switch the focus from textual data to another modality of cultural data by presenting a Virtual Museum that combines interactive visualizations and computer vision in order to explore a collection of artworks. With the lessons learned from the previous projects, we engage in the labeling and analysis of medieval illuminated manuscripts and so combine some of the machine learning methods and visualizations that were used for textual data with computer vision methods. Finally, we give reflections on the interdisciplinary projects and the lessons learned, before we discuss existing challenges when working with cultural heritage data from the computer science perspective to outline potential research directions for machine learning and visual analytics of cultural heritage data

Applications of digital and innovative construction techniques in lower-income countries

Construction technology has experienced rapid changes in recent years associated with the growing use of computers, software development, automation and offsite construction. These advances are helping to address two common problems associated with the industry, namely project delay and safety. A lack of communication between stakeholders and the uncertainties associated with construction sites and processes have been identified as the main causes of construction delays for a number of years. However, recent developments in information and communications technologies (ICT), new cutting-edge technologies (NCETs) and modern methods of construction (MMC) are helping the construction industry to complete projects on time and to budget by improving communications between stakeholders and their pre-engagement with a project. This report reviews examples of successful adoption of digital and innovative construction technologies in low-income countries in the three areas of 1) Building Information Modelling (BIM), 2) Offsite construction, and 3) New cutting-edge technologies (NCETs)